An Agent-Based Application for Automatic Classification of Food Allergies and Intolerances in Recipes
The automatic recommendation of recipes for users with some kind of food allergies or intolerances is still a complex and open problem. One of the limitations is the lack of databases that labels ingredients of recipes with their associated allergens. This limitation may cause the recommendation of inappropriate recipes to people with specific food restrictions. In order to try to solve this, this paper proposes a collaborative multi-agent system that automatically detects food allergies in nutrients and labels ingredients with their potential allergens. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations.
KeywordsRecommendation system Food allergy Multi-agent system
This work was supported by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politècnica de València.
- 5.Kolodner, J.L.: Capitalizing on failure through case-based inference. Technical report, DTIC Document (1987)Google Scholar
- 7.Phanich, M., Pholkul, P., Phimoltares, S.: Food recommendation system using clustering analysis for diabetic patients. In: 2010 International Conference on Information Science and Applications (ICISA), pp. 1–8. IEEE (2010)Google Scholar
- 8.Teng, C.-Y., Lin, Y.-R., Adamic, L.A.: Recipe recommendation using ingredient networks. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 298–307. ACM (2012)Google Scholar
- 9.Freyne, J., Berkovsky, S.: Intelligent food planning: personalized recipe recommendation. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 321–324. ACM (2010)Google Scholar
- 10.Ueda, M., Takahata, M., Nakajima, S.: User’s food preference extraction for personalized cooking recipe recommendation. In: Semantic Personalized Information Management: Retrieval and Recommendation SPIM 2011, p. 98 (2011)Google Scholar
- 12.Elsweiler, D., Harvey, M.: Towards automatic meal plan recommendations for balanced nutrition. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 313–316. ACM (2015)Google Scholar
- 14.Schall, D.: Social network-based recommender systems (2015)Google Scholar